TY  - JOUR
N2  - Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deeplearning- based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the GI tract (esophagus, stomach, duodenum) and surrounding organs (liver, spleen, left kidney, gallbladder). We directly compared the segmentation accuracy of the proposed method to existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 vs. 0.71, 0.74 and 0.74 for the pancreas, 0.90 vs 0.85, 0.87 and 0.83 for the stomach and 0.76 vs 0.68, 0.69 and 0.66 for the esophagus. We conclude that deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.
ID  - discovery10044219
KW  - Abdominal CT
KW  -  Segmentation
KW  -  Deep learning
KW  -  Pancreas
KW  -  Gastrointestinal tract
KW  -  Stomach
KW  -  Duodenum
KW  -  Esophagus
KW  -  Liver
KW  -  Spleen
KW  -  Kidney
KW  -  Gallbladder
TI  - Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks
AV  - public
Y1  - 2018/02/14/
EP  - 1834
UR  - https://doi.org/10.1109/TMI.2018.2806309
SN  - 0278-0062
JF  - IEEE Transactions on Medical Imaging
A1  - Gibson, E
A1  - Giganti, F
A1  - Hu, Y
A1  - Bonmati, E
A1  - Bandula, S
A1  - Gurusamy, K
A1  - Davidson, B
A1  - Pereira, SP
A1  - Clarkson, MJ
A1  - Barratt, DC
VL  - 37
SP  - 1822
IS  - 8
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -